Learning CenterLearning Topics
Data Analytics vs. Data Analysis
Quantitative vs. Qualitative Data
What is Behavioral Analytics?
Data Analytics vs. Business Analytics
Data Analytics vs. Data Science
The Difference Between Data Analytics and Statistics
The Difference Between Data Analytics and Data Visualization
Data Analytics Lifecycle
Data Analytics vs Business Intelligence
What is Descriptive Analytics?
What is Data Analytics?
What is Diagnostic Analytics?
Data Analytics Processes
A top-level guide to data lakes
Redshift vs Snowflake vs BigQuery: Choosing a Warehouse
Data Warehouse Architecture
What Is a Data Warehouse?
How to Create and Use Business Intelligence with a Data Warehouse
Best Practices for Accessing Your Data Warehouse
Data Warehouse Best Practices — preparing your data for peak performance
How do Data Warehouses Enhance Data Mining?
Data Warehouses versus Databases: What’s the Difference?
What are the Benefits of a Data Warehouse?
Key Concepts of a Data Warehouse
Data Warehouses versus Data Lakes
Data Warehouses versus Data Marts
Difference Between Big Data and Data Warehouses
How to Move Data in Data Warehouses
What Is Customer Data?
Customer Data Analytics
Customer Data Management
Collecting Customer Data
The Importance of First-Party Customer Data After iOS Updates
Types of Customer Data
What Is a Customer Data Platform?
What is an Identity Graph?
Customer Data Protection
A complete guide to first-party customer data
CDPs vs. DMPs
What is Identity Resolution?
What is Consent Management?
Data Access Control
Data Sharing and Third Parties
What is PII Masking and How Can You Use It?
Data Security Strategies
Data Security Technologies
Data Protection Security Controls
How to Manage Data Retention
How To Handle Your Company’s Sensitive Data
Data Security Best Practices For Companies
What is Persistent Data?
Google Analytics 4 and eCommerce Tracking
What Is Google Analytics 4 and Why Should You Migrate?
GA4 Migration Guide
GA4 vs. Universal Analytics
What are the New Features of Google Analytics 4 (GA4)?
Benefits and Limitations of Google Analytics 4 (GA4)
Understanding Google Analytics 4 Organization Hierarchy
Understanding Data Streams in Google Analytics 4
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What Is a Customer Data Platform?
A customer data platform (CDP) is a software system that collects customer data, builds a unified view of your customers (by building profiles), and then uses those profiles to add value across the company by:
- Gaining deeper insights into customers via analytics
- Enriching the quality of customer interactions
- Pushing customer intelligence to key teams such as sales, marketing or customer support.
- Controlling governance and privacy for customer data
Ideally, the database that the CDP leverages should unify all customer data and be accessible to other systems within your company, though that isn’t the case for many CDP vendors. Regardless, the key elements of a CDP — according to the CDP Institute, a vendor-neutral organization dedicated to helping companies manage customer data — are:
- Packaged software — a CDP is a prebuilt system configured to meet each client’s needs. CDPs require less technical resources and skill to set up than a similar data warehouse, reducing the associated time, cost, and risk.
- A persistent, unified customer database — a CDP creates a comprehensive view of customers by capturing data from multiple systems, linking related information, and storing the information to track behavior over time.
- Accessible to other systems — CDP data can be used by other systems for analysis and to manage customer interactions.
CDPs vs. DMPs vs. CRMs
As there is often confusion between customer data platforms, data management platforms (DMPs), and customer relationship management (CRM) software solutions, it’s worthwhile to explicitly note their different purposes.
- CDPs collect and aggregate first-party customer data to build an integrated, coherent customer profile. This profile blends customers’ personally identifiable information (PII), online behavior, and demographic and aspirational information and is often also enriched with third party data. CDPs are used to enable various teams to understand and act on customer data.
- DMPs typically gather anonymized data from web cookies for the purposes of advertising targeting and retain it for less than a few months. DMPs are almost always used by advertising teams.
- CRMs track relationships with customers but don’t usually include data on how customers interact with your business’s products and services (behavioral data) or their transactions (payment and purchase data).
|Data scale||Wide and deep spectrum of customer data||Anonymized, based on cookies||Fine-grained business data|
|Typical purpose||Unify customer data, gain insights and take action||Achieve better targeting for advertising campaigns||Track status of customer relationships over time|
|Most common users||Widespread usage by anyone who consumes customer data internally||Paid advertising teams||Sales and customer support teams|
|Data sources||Primarily first part data||Primarily closed third party data sets||First party data, often enriched with public data sets|
CDPs vs. data warehouses vs. data lakes
We’ve covered both data warehouses and data lakes elsewhere, but it’s worth the effort to summarize the primary differences.
CDPs primarily deal with first-party customer data; that is, data that was either supplied directly by the customer, or inferred from their actions and behavior within your product or service offering. The collected data is specifically organized to allow business users to generate actionable insights into customer behavior.
The core value a CDP brings to a software product is its integration across the data stack. By relying on a CDP, it’s possible to identify users, create user profiles, and keep track of users’ behavior and preferences in various parts of the application, from the front end to each back-end microservice.
Data warehouses can manage structured data of almost any scale. The data stored in a warehouse is usually extracted from a variety of sources, but before storage, the data is transformed into a standardized format.
The most valuable aspect of a data warehouse is the structure it provides: it allows the data from the warehouse to be used directly as an input into artificial intelligence prediction models, for business intelligence analysis, and for other business purposes.
Data lakes can manage multiple kinds of unstructured data. The data is made available for quick ad hoc queries, but usually requires further processing before it can be used in other systems and business processes. What makes data lakes uniquely valuable is the high volume of storage they provide, as well as the ability to manage and query unstructured data at low cost.
Customer data platforms come in many flavors
CDPs vary significantly in functionality across the market. Some products offer a full-fledged solution that includes a data warehouse; some only offer an interface on top of an existing data warehouse, while some offer a hybrid model where you can bring your own data warehouse but the CDP powers its service offering primarily from storing your data in its warehouse. The exact way a CDP is set up tends to be organization-specific and doesn’t translate well across organizations. At some organizations, CDPs also take on functions of other data systems - for example, they become the primary data warehouse.
Various CDP vendors also offer different perspectives on who should be responsible for the CDP within the organization. Traditionally, CDP products were built for marketing teams, but many modern companies use a CDP that’s maintained by their engineering team, or a dedicated data operations team, enabling CDPs to add value across the company’s systems. Some CDP products are open-source, enabling engineering teams to customize the platform to their company’s needs.
CDP business rationale: Build vs. Buy
The underlying goal for most businesses implementing a CDP is to improve their customers’ experience. While such an improvement sounds straightforward, it’s everything but straightforward for a large business with thousands of employees and numerous stakeholders involved in business decisions.
Soumyadeb Mitra, RudderStack’s CEO, goes into the details of how to address this challenge in the post Build or Buy? Lessons From Ten Years Building Customer Data Pipelines. In short, to give the right teams the necessary information about what needs to be changed to improve customer experience, the following steps are needed:
- Collect information about each interaction a customer has with your business.
- Unify that data and store it in a centralized location.
- Enrich the data and integrate it into other tools used by product, development, marketing, and sales teams.
Once the decision is made on implementing a CDP, there is another important decision to make: should you build your own CDP, or should you implement a third-party product instead?
For those choosing to build, the common challenges include:
- Scaling the infrastructure to support millions of events per day and more.
- Integrating the CDP with all the necessary data sources, such as databases, other SaaS products, and advertising platforms like Google Ads.
- Transforming the data into a standardized format and ensuring the data is being updated correctly with new events.
- Dealing with privacy regulations that affect customer data.
- Being able to support internal demand for new functionality or integrations, while maintaining existing ones (e.g. changes in destination API versions, deprecated endpoints, etc.)
Those buying a third-party solution will likely run into issues like:
- Data lock-in: inability to fully leverage the data you collect because of how and where it’s stored by the vendor’s product.
- Lack of customization options and support for complex use cases.
- High cost, especially for vendors that both process and store your data.
- High cost of switching: Since CDP data collection and destinations are often core to supporting business processes, replacing such a core component of your business requires significant planning and resources.
- Potential regulatory requirements: If you cannot send, store, or process your data outside your own infrastructure, certain types of CDPs may not even be an option in the first place.
The challenges of building your own CDP are highly complex, and using third-party software can be a way to avoid the headache of self-building. It’s worth considering whether you have the resources to build your own CDP, as well as evaluating the cost of any third-party software and the level of control you will have over it, before making a decision.
Next steps in customer data platform development
Properly planned CDPs give your business an edge in establishing, maintaining, and refining the customer experience and driving increased sales and profit through better-developed leads. It is also important to think about CDPs in the larger context of Customer Data Infrastructure (CDI). A CDP is the engine powering your data collection and processing, but there is also data storage, data modeling, data pipelines, and more to think about for a robust tech stack.
You can learn more about customer data in our corpus of related articles, including:
- What is Customer Data?
- Types of Customer Data
- Customer Data Management
- Customer Data Protection
- How to Collect Customer Data
Together, these articles will help you to chart your path to establishing a customer data platform and improving your marketing reach.